Análisis PPM-S
library(sjPlot)
library(dplyr)
library(lavaan)
data01 <- sjlabelled::read_spss(path = "data/Estudio_3_ola1_January_9_2020.sav",verbose = FALSE)
dat01 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01")) %>% na.omit()
dat02 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv02")) %>% na.omit()
dat03 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv03_p")) %>% na.omit()
Version 01:
- Percepcion esfuerzo
- Percepcion talento
- Percepcion familia rica
- Percepcion redes
- Preferencia esfuerzo
- Preferencia talento
- Preferencia familia rica
- Preferencia redes
model01 <- 'perc_merit=~meritv01_perc_effort+meritv01_perc_talent
perc_nmerit=~meritv01_perc_wpart+meritv01_perc_netw
pref_merit=~meritv01_pref_effort+meritv01_pref_talent
pref_nmerit=~meritv01_pref_wpart+meritv01_pref_netw'
fit1 <- cfa(model = model01,data = dat01,ordered = c("meritv01_perc_effort","meritv01_perc_talent",
"meritv01_perc_wpart","meritv01_perc_netw",
"meritv01_pref_effort","meritv01_pref_talent",
"meritv01_pref_wpart","meritv01_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit1,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit1,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 55 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 712
##
## Estimator DWLS Robust
## Model Fit Test Statistic 25.631 42.276
## Degrees of freedom 14 14
## P-value (Chi-square) 0.029 0.000
## Scaling correction factor 0.650
## Shift parameter 2.823
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 5947.085 4084.523
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.998 0.993
## Tucker-Lewis Index (TLI) 0.996 0.986
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.034 0.053
## 90 Percent Confidence Interval 0.011 0.055 0.035 0.072
## P-value RMSEA <= 0.05 0.890 0.354
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.032 0.032
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv01_prc_ffr 1.000 0.689 0.689
## mrtv01_prc_tln 1.177 0.100 11.799 0.000 0.810 0.810
## perc_nmerit =~
## mrtv01_prc_wpr 1.000 0.850 0.850
## mrtv01_prc_ntw 1.102 0.061 18.198 0.000 0.936 0.936
## pref_merit =~
## mrtv01_prf_ffr 1.000 0.848 0.848
## mrtv01_prf_tln 0.758 0.053 14.407 0.000 0.643 0.643
## pref_nmerit =~
## mrtv01_prf_wpr 1.000 0.545 0.545
## mrtv01_prf_ntw 2.302 0.754 3.054 0.002 1.255 1.255
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit 0.016 0.027 0.603 0.546 0.028 0.028
## pref_merit 0.326 0.033 9.966 0.000 0.559 0.559
## pref_nmerit 0.064 0.028 2.317 0.020 0.172 0.172
## perc_nmerit ~~
## pref_merit 0.379 0.034 11.290 0.000 0.526 0.526
## pref_nmerit -0.035 0.021 -1.684 0.092 -0.077 -0.077
## pref_merit ~~
## pref_nmerit 0.023 0.020 1.150 0.250 0.051 0.051
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv01_prc_ffr 0.000 0.000 0.000
## .mrtv01_prc_tln 0.000 0.000 0.000
## .mrtv01_prc_wpr 0.000 0.000 0.000
## .mrtv01_prc_ntw 0.000 0.000 0.000
## .mrtv01_prf_ffr 0.000 0.000 0.000
## .mrtv01_prf_tln 0.000 0.000 0.000
## .mrtv01_prf_wpr 0.000 0.000 0.000
## .mrtv01_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv01_prc_f|1 -1.023 0.057 -17.909 0.000 -1.023 -1.023
## mrtv01_prc_f|2 -0.401 0.048 -8.290 0.000 -0.401 -0.401
## mrtv01_prc_f|3 0.120 0.047 2.546 0.011 0.120 0.120
## mrtv01_prc_f|4 0.809 0.053 15.251 0.000 0.809 0.809
## mrtv01_prc_t|1 -1.259 0.063 -19.863 0.000 -1.259 -1.259
## mrtv01_prc_t|2 -0.417 0.049 -8.587 0.000 -0.417 -0.417
## mrtv01_prc_t|3 0.444 0.049 9.105 0.000 0.444 0.444
## mrtv01_prc_t|4 1.324 0.066 20.211 0.000 1.324 1.324
## mrtv01_prc_w|1 -1.192 0.061 -19.416 0.000 -1.192 -1.192
## mrtv01_prc_w|2 -0.775 0.053 -14.762 0.000 -0.775 -0.775
## mrtv01_prc_w|3 -0.227 0.047 -4.790 0.000 -0.227 -0.227
## mrtv01_prc_w|4 0.397 0.048 8.216 0.000 0.397 0.397
## mrtv01_prc_n|1 -1.275 0.064 -19.955 0.000 -1.275 -1.275
## mrtv01_prc_n|2 -0.943 0.055 -17.012 0.000 -0.943 -0.943
## mrtv01_prc_n|3 -0.463 0.049 -9.475 0.000 -0.463 -0.463
## mrtv01_prc_n|4 0.448 0.049 9.179 0.000 0.448 0.448
## mrtv01_prf_f|1 -1.405 0.068 -20.530 0.000 -1.405 -1.405
## mrtv01_prf_f|2 -1.104 0.059 -18.695 0.000 -1.104 -1.104
## mrtv01_prf_f|3 -0.564 0.050 -11.313 0.000 -0.564 -0.564
## mrtv01_prf_f|4 0.216 0.047 4.566 0.000 0.216 0.216
## mrtv01_prf_t|1 -1.316 0.065 -20.170 0.000 -1.316 -1.316
## mrtv01_prf_t|2 -0.729 0.052 -14.056 0.000 -0.729 -0.729
## mrtv01_prf_t|3 0.188 0.047 3.968 0.000 0.188 0.188
## mrtv01_prf_t|4 0.966 0.056 17.273 0.000 0.966 0.966
## mrtv01_prf_w|1 -0.738 0.052 -14.198 0.000 -0.738 -0.738
## mrtv01_prf_w|2 -0.106 0.047 -2.247 0.025 -0.106 -0.106
## mrtv01_prf_w|3 0.829 0.053 15.528 0.000 0.829 0.829
## mrtv01_prf_w|4 1.793 0.088 20.388 0.000 1.793 1.793
## mrtv01_prf_n|1 -0.610 0.050 -12.115 0.000 -0.610 -0.610
## mrtv01_prf_n|2 0.134 0.047 2.845 0.004 0.134 0.134
## mrtv01_prf_n|3 1.110 0.059 18.753 0.000 1.110 1.110
## mrtv01_prf_n|4 2.005 0.104 19.276 0.000 2.005 2.005
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv01_prc_ffr 0.526 0.526 0.526
## .mrtv01_prc_tln 0.343 0.343 0.343
## .mrtv01_prc_wpr 0.278 0.278 0.278
## .mrtv01_prc_ntw 0.123 0.123 0.123
## .mrtv01_prf_ffr 0.281 0.281 0.281
## .mrtv01_prf_tln 0.587 0.587 0.587
## .mrtv01_prf_wpr 0.703 0.703 0.703
## .mrtv01_prf_ntw -0.576 -0.576 -0.576
## perc_merit 0.474 0.048 9.827 0.000 1.000 1.000
## perc_nmerit 0.722 0.043 16.749 0.000 1.000 1.000
## pref_merit 0.719 0.052 13.732 0.000 1.000 1.000
## pref_nmerit 0.297 0.099 3.014 0.003 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv01_prc_ffr 1.000 1.000 1.000
## mrtv01_prc_tln 1.000 1.000 1.000
## mrtv01_prc_wpr 1.000 1.000 1.000
## mrtv01_prc_ntw 1.000 1.000 1.000
## mrtv01_prf_ffr 1.000 1.000 1.000
## mrtv01_prf_tln 1.000 1.000 1.000
## mrtv01_prf_wpr 1.000 1.000 1.000
## mrtv01_prf_ntw 1.000 1.000 1.000
Version 02:
- Percepcion esfuerzo
- Preferencia esfuerzo
- Percepcion talento
- Preferencia talento
- Percepcion familia rica
- Preferencia familia rica
- Percepcion redes
- Preferencia redes
model02 <- 'perc_merit=~meritv02_perc_effort+meritv02_perc_talent
perc_nmerit=~meritv02_perc_wpart+meritv02_perc_netw
pref_merit=~meritv02_pref_effort+meritv02_pref_talent
pref_nmerit=~meritv02_pref_wpart+meritv02_pref_netw'
fit2 <- cfa(model = model02,data = dat02,ordered = c("meritv02_perc_effort","meritv02_perc_talent",
"meritv02_perc_wpart","meritv02_perc_netw",
"meritv02_pref_effort","meritv02_pref_talent",
"meritv02_pref_wpart","meritv02_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit2,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit2,what = "std")
## lavaan 0.6-4 ended normally after 34 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 717
##
## Estimator DWLS Robust
## Model Fit Test Statistic 67.652 107.573
## Degrees of freedom 14 14
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.641
## Shift parameter 1.952
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3301.803 2410.566
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.984 0.961
## Tucker-Lewis Index (TLI) 0.967 0.921
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.073 0.097
## 90 Percent Confidence Interval 0.056 0.091 0.080 0.114
## P-value RMSEA <= 0.05 0.013 0.000
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.050 0.050
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv02_prc_ffr 1.000 0.758 0.758
## mrtv02_prc_tln 0.944 0.058 16.361 0.000 0.716 0.716
## perc_nmerit =~
## mrtv02_prc_wpr 1.000 0.842 0.842
## mrtv02_prc_ntw 0.965 0.100 9.614 0.000 0.812 0.812
## pref_merit =~
## mrtv02_prf_ffr 1.000 0.816 0.816
## mrtv02_prf_tln 0.795 0.053 14.957 0.000 0.649 0.649
## pref_nmerit =~
## mrtv02_prf_wpr 1.000 1.044 1.044
## mrtv02_prf_ntw 0.498 0.143 3.488 0.000 0.520 0.520
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit 0.031 0.031 1.001 0.317 0.049 0.049
## pref_merit 0.448 0.030 14.761 0.000 0.723 0.723
## pref_nmerit 0.210 0.034 6.163 0.000 0.265 0.265
## perc_nmerit ~~
## pref_merit 0.295 0.035 8.353 0.000 0.430 0.430
## pref_nmerit 0.085 0.035 2.396 0.017 0.097 0.097
## pref_merit ~~
## pref_nmerit 0.135 0.035 3.804 0.000 0.158 0.158
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv02_prc_ffr 0.000 0.000 0.000
## .mrtv02_prc_tln 0.000 0.000 0.000
## .mrtv02_prc_wpr 0.000 0.000 0.000
## .mrtv02_prc_ntw 0.000 0.000 0.000
## .mrtv02_prf_ffr 0.000 0.000 0.000
## .mrtv02_prf_tln 0.000 0.000 0.000
## .mrtv02_prf_wpr 0.000 0.000 0.000
## .mrtv02_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv02_prc_f|1 -0.943 0.055 -17.062 0.000 -0.943 -0.943
## mrtv02_prc_f|2 -0.329 0.048 -6.892 0.000 -0.329 -0.329
## mrtv02_prc_f|3 0.170 0.047 3.618 0.000 0.170 0.170
## mrtv02_prc_f|4 0.785 0.052 14.960 0.000 0.785 0.785
## mrtv02_prc_t|1 -1.303 0.065 -20.178 0.000 -1.303 -1.303
## mrtv02_prc_t|2 -0.469 0.049 -9.626 0.000 -0.469 -0.469
## mrtv02_prc_t|3 0.385 0.048 8.003 0.000 0.385 0.385
## mrtv02_prc_t|4 1.328 0.065 20.300 0.000 1.328 1.328
## mrtv02_prc_w|1 -1.175 0.061 -19.356 0.000 -1.175 -1.175
## mrtv02_prc_w|2 -0.785 0.052 -14.960 0.000 -0.785 -0.785
## mrtv02_prc_w|3 -0.322 0.048 -6.743 0.000 -0.322 -0.322
## mrtv02_prc_w|4 0.344 0.048 7.188 0.000 0.344 0.344
## mrtv02_prc_n|1 -1.381 0.067 -20.519 0.000 -1.381 -1.381
## mrtv02_prc_n|2 -0.954 0.055 -17.193 0.000 -0.954 -0.954
## mrtv02_prc_n|3 -0.473 0.049 -9.700 0.000 -0.473 -0.473
## mrtv02_prc_n|4 0.517 0.049 10.507 0.000 0.517 0.517
## mrtv02_prf_f|1 -1.121 0.059 -18.914 0.000 -1.121 -1.121
## mrtv02_prf_f|2 -0.874 0.054 -16.197 0.000 -0.874 -0.874
## mrtv02_prf_f|3 -0.513 0.049 -10.434 0.000 -0.513 -0.513
## mrtv02_prf_f|4 0.366 0.048 7.633 0.000 0.366 0.366
## mrtv02_prf_t|1 -1.303 0.065 -20.178 0.000 -1.303 -1.303
## mrtv02_prf_t|2 -0.574 0.050 -11.530 0.000 -0.574 -0.574
## mrtv02_prf_t|3 0.318 0.048 6.669 0.000 0.318 0.318
## mrtv02_prf_t|4 1.102 0.059 18.741 0.000 1.102 1.102
## mrtv02_prf_w|1 -0.795 0.053 -15.100 0.000 -0.795 -0.795
## mrtv02_prf_w|2 -0.271 0.047 -5.703 0.000 -0.271 -0.271
## mrtv02_prf_w|3 0.553 0.050 11.165 0.000 0.553 0.553
## mrtv02_prf_w|4 1.532 0.073 20.855 0.000 1.532 1.532
## mrtv02_prf_n|1 -0.667 0.051 -13.120 0.000 -0.667 -0.667
## mrtv02_prf_n|2 0.153 0.047 3.246 0.001 0.153 0.153
## mrtv02_prf_n|3 0.965 0.056 17.323 0.000 0.965 0.965
## mrtv02_prf_n|4 1.779 0.087 20.510 0.000 1.779 1.779
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv02_prc_ffr 0.425 0.425 0.425
## .mrtv02_prc_tln 0.488 0.488 0.488
## .mrtv02_prc_wpr 0.292 0.292 0.292
## .mrtv02_prc_ntw 0.340 0.340 0.340
## .mrtv02_prf_ffr 0.333 0.333 0.333
## .mrtv02_prf_tln 0.579 0.579 0.579
## .mrtv02_prf_wpr -0.090 -0.090 -0.090
## .mrtv02_prf_ntw 0.730 0.730 0.730
## perc_merit 0.575 0.044 13.145 0.000 1.000 1.000
## perc_nmerit 0.708 0.077 9.254 0.000 1.000 1.000
## pref_merit 0.667 0.055 12.183 0.000 1.000 1.000
## pref_nmerit 1.090 0.307 3.554 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv02_prc_ffr 1.000 1.000 1.000
## mrtv02_prc_tln 1.000 1.000 1.000
## mrtv02_prc_wpr 1.000 1.000 1.000
## mrtv02_prc_ntw 1.000 1.000 1.000
## mrtv02_prf_ffr 1.000 1.000 1.000
## mrtv02_prf_tln 1.000 1.000 1.000
## mrtv02_prf_wpr 1.000 1.000 1.000
## mrtv02_prf_ntw 1.000 1.000 1.000
Version 03: orden aleatorio
model03 <- 'perc_merit=~meritv03_perc_effort+meritv03_perc_talent
perc_nmerit=~meritv03_perc_wpart+meritv03_perc_netw
pref_merit=~meritv03_pref_effort+meritv03_pref_talent
pref_nmerit=~meritv03_pref_wpart+meritv03_pref_netw'
fit3 <- cfa(model = model03,data = dat03,ordered = c("meritv03_perc_effort","meritv03_perc_talent",
"meritv03_perc_wpart","meritv03_perc_netw",
"meritv03_pref_effort","meritv03_pref_talent",
"meritv03_pref_wpart","meritv03_pref_netw"))
summary(fit3,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit3,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 36 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 712
##
## Estimator DWLS Robust
## Model Fit Test Statistic 41.633 63.336
## Degrees of freedom 14 14
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.680
## Shift parameter 2.116
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3012.318 2326.099
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.991 0.979
## Tucker-Lewis Index (TLI) 0.981 0.957
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.070
## 90 Percent Confidence Interval 0.035 0.072 0.053 0.088
## P-value RMSEA <= 0.05 0.375 0.026
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042 0.042
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv03_prc_ffr 1.000 0.704 0.704
## mrtv03_prc_tln 0.929 0.140 6.628 0.000 0.654 0.654
## perc_nmerit =~
## mrtv03_prc_wpr 1.000 0.807 0.807
## mrtv03_prc_ntw 1.105 0.116 9.508 0.000 0.892 0.892
## pref_merit =~
## mrtv03_prf_ffr 1.000 0.658 0.658
## mrtv03_prf_tln 0.897 0.099 9.029 0.000 0.591 0.591
## pref_nmerit =~
## mrtv03_prf_wpr 1.000 0.781 0.781
## mrtv03_prf_ntw 0.987 0.169 5.839 0.000 0.771 0.771
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit -0.025 0.029 -0.866 0.386 -0.044 -0.044
## pref_merit 0.212 0.033 6.420 0.000 0.457 0.457
## pref_nmerit 0.164 0.033 4.915 0.000 0.298 0.298
## perc_nmerit ~~
## pref_merit 0.265 0.035 7.614 0.000 0.500 0.500
## pref_nmerit -0.037 0.030 -1.228 0.219 -0.059 -0.059
## pref_merit ~~
## pref_nmerit 0.095 0.032 2.943 0.003 0.185 0.185
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv03_prc_ffr 0.000 0.000 0.000
## .mrtv03_prc_tln 0.000 0.000 0.000
## .mrtv03_prc_wpr 0.000 0.000 0.000
## .mrtv03_prc_ntw 0.000 0.000 0.000
## .mrtv03_prf_ffr 0.000 0.000 0.000
## .mrtv03_prf_tln 0.000 0.000 0.000
## .mrtv03_prf_wpr 0.000 0.000 0.000
## .mrtv03_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv03_prc_f|1 -1.185 0.061 -19.363 0.000 -1.185 -1.185
## mrtv03_prc_f|2 -0.448 0.049 -9.179 0.000 -0.448 -0.448
## mrtv03_prc_f|3 -0.056 0.047 -1.198 0.231 -0.056 -0.056
## mrtv03_prc_f|4 0.653 0.051 12.839 0.000 0.653 0.653
## mrtv03_prc_t|1 -1.207 0.062 -19.519 0.000 -1.207 -1.207
## mrtv03_prc_t|2 -0.348 0.048 -7.250 0.000 -0.348 -0.348
## mrtv03_prc_t|3 0.271 0.048 5.686 0.000 0.271 0.271
## mrtv03_prc_t|4 1.029 0.057 17.971 0.000 1.029 1.029
## mrtv03_prc_w|1 -1.244 0.063 -19.768 0.000 -1.244 -1.244
## mrtv03_prc_w|2 -0.780 0.053 -14.832 0.000 -0.780 -0.780
## mrtv03_prc_w|3 -0.401 0.048 -8.290 0.000 -0.401 -0.401
## mrtv03_prc_w|4 0.274 0.048 5.760 0.000 0.274 0.274
## mrtv03_prc_n|1 -1.395 0.068 -20.499 0.000 -1.395 -1.395
## mrtv03_prc_n|2 -1.041 0.058 -18.095 0.000 -1.041 -1.041
## mrtv03_prc_n|3 -0.593 0.050 -11.824 0.000 -0.593 -0.593
## mrtv03_prc_n|4 0.234 0.047 4.939 0.000 0.234 0.234
## mrtv03_prf_f|1 -1.495 0.072 -20.740 0.000 -1.495 -1.495
## mrtv03_prf_f|2 -1.207 0.062 -19.519 0.000 -1.207 -1.207
## mrtv03_prf_f|3 -0.752 0.052 -14.410 0.000 -0.752 -0.752
## mrtv03_prf_f|4 0.131 0.047 2.771 0.006 0.131 0.131
## mrtv03_prf_t|1 -1.368 0.067 -20.399 0.000 -1.368 -1.368
## mrtv03_prf_t|2 -0.648 0.051 -12.766 0.000 -0.648 -0.648
## mrtv03_prf_t|3 0.088 0.047 1.872 0.061 0.088 0.088
## mrtv03_prf_t|4 0.799 0.053 15.112 0.000 0.799 0.799
## mrtv03_prf_w|1 -0.911 0.055 -16.615 0.000 -0.911 -0.911
## mrtv03_prf_w|2 -0.224 0.047 -4.715 0.000 -0.224 -0.224
## mrtv03_prf_w|3 0.568 0.050 11.386 0.000 0.568 0.568
## mrtv03_prf_w|4 1.454 0.070 20.663 0.000 1.454 1.454
## mrtv03_prf_n|1 -0.785 0.053 -14.902 0.000 -0.785 -0.785
## mrtv03_prf_n|2 0.116 0.047 2.471 0.013 0.116 0.116
## mrtv03_prf_n|3 0.790 0.053 14.972 0.000 0.790 0.790
## mrtv03_prf_n|4 1.667 0.080 20.723 0.000 1.667 1.667
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv03_prc_ffr 0.504 0.504 0.504
## .mrtv03_prc_tln 0.572 0.572 0.572
## .mrtv03_prc_wpr 0.349 0.349 0.349
## .mrtv03_prc_ntw 0.205 0.205 0.205
## .mrtv03_prf_ffr 0.567 0.567 0.567
## .mrtv03_prf_tln 0.651 0.651 0.651
## .mrtv03_prf_wpr 0.390 0.390 0.390
## .mrtv03_prf_ntw 0.406 0.406 0.406
## perc_merit 0.496 0.079 6.254 0.000 1.000 1.000
## perc_nmerit 0.651 0.072 9.056 0.000 1.000 1.000
## pref_merit 0.433 0.060 7.268 0.000 1.000 1.000
## pref_nmerit 0.610 0.107 5.714 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv03_prc_ffr 1.000 1.000 1.000
## mrtv03_prc_tln 1.000 1.000 1.000
## mrtv03_prc_wpr 1.000 1.000 1.000
## mrtv03_prc_ntw 1.000 1.000 1.000
## mrtv03_prf_ffr 1.000 1.000 1.000
## mrtv03_prf_tln 1.000 1.000 1.000
## mrtv03_prf_wpr 1.000 1.000 1.000
## mrtv03_prf_ntw 1.000 1.000 1.000
Version 04: muestra completa
dat04 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01"),starts_with("meritv02"),starts_with("meritv03_p"))
dat04$perc_effort <- rowSums(dat04[,c(matches(match = "perc_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_talent <- rowSums(dat04[,c(matches(match = "perc_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_wpart <- rowSums(dat04[,c(matches(match = "perc_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$perc_netw <- rowSums(dat04[,c(matches(match = "perc_netw" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_effort <- rowSums(dat04[,c(matches(match = "pref_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_talent <- rowSums(dat04[,c(matches(match = "pref_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_wpart <- rowSums(dat04[,c(matches(match = "pref_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_netw <- rowSums(dat04[,c(matches(match = "pref_netw" ,vars = names(dat04)))],na.rm = TRUE)
model04 <- 'perc_merit=~perc_effort+perc_talent
perc_nmerit=~perc_wpart+perc_netw
pref_merit=~pref_effort+pref_talent
pref_nmerit=~pref_wpart+pref_netw'
fit4 <- cfa(model = model04,data = dat04)
summary(fit4,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit4,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 43 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Number of observations 2236
##
## Estimator ML
## Model Fit Test Statistic 209.300
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 6899.153
## Degrees of freedom 28
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.972
## Tucker-Lewis Index (TLI) 0.943
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -27655.437
## Loglikelihood unrestricted model (H1) -27550.787
##
## Number of free parameters 22
## Akaike (AIC) 55354.875
## Bayesian (BIC) 55480.549
## Sample-size adjusted Bayesian (BIC) 55410.651
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent Confidence Interval 0.070 0.089
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.029
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## perc_effort 1.000 1.121 0.750
## perc_talent 0.873 0.033 26.293 0.000 0.979 0.759
## perc_nmerit =~
## perc_wpart 1.000 1.246 0.818
## perc_netw 1.038 0.030 34.064 0.000 1.294 0.901
## pref_merit =~
## pref_effort 1.000 1.179 0.809
## pref_talent 0.797 0.026 30.459 0.000 0.939 0.704
## pref_nmerit =~
## pref_wpart 1.000 1.054 0.829
## pref_netw 0.851 0.042 20.250 0.000 0.896 0.756
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit 0.535 0.042 12.817 0.000 0.383 0.383
## pref_merit 0.985 0.049 20.205 0.000 0.745 0.745
## pref_nmerit 0.579 0.039 14.949 0.000 0.490 0.490
## perc_nmerit ~~
## pref_merit 1.029 0.050 20.443 0.000 0.701 0.701
## pref_nmerit 0.443 0.037 11.923 0.000 0.337 0.337
## pref_merit ~~
## pref_nmerit 0.543 0.038 14.193 0.000 0.437 0.437
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .perc_effort 0.977 0.050 19.636 0.000 0.977 0.437
## .perc_talent 0.703 0.037 18.900 0.000 0.703 0.423
## .perc_wpart 0.767 0.044 17.383 0.000 0.767 0.331
## .perc_netw 0.386 0.042 9.148 0.000 0.386 0.187
## .pref_effort 0.733 0.041 17.817 0.000 0.733 0.345
## .pref_talent 0.897 0.035 25.814 0.000 0.897 0.504
## .pref_wpart 0.505 0.053 9.616 0.000 0.505 0.313
## .pref_netw 0.603 0.041 14.843 0.000 0.603 0.429
## perc_merit 1.258 0.072 17.381 0.000 1.000 1.000
## perc_nmerit 1.552 0.076 20.556 0.000 1.000 1.000
## pref_merit 1.390 0.069 20.143 0.000 1.000 1.000
## pref_nmerit 1.111 0.068 16.307 0.000 1.000 1.000